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Information
Special Issues
Big Data Analytics and Computational Intelligence
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Big Data Analytics and Computational Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (20 November 2019) | Viewed by 17187

Special Issue Editors

Prof. Dr. Ajith Abraham

E-Mail Website
Guest Editor
Machine Intelligence Research Labs (MIR Labs), 3rd Street NW, P.O. Box 2259, Auburn, WA 98071, USA
Interests: artificial intelligence; data mining; big data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedro Isaias

E-Mail Website
Guest Editor
UQ Business School, University of Queensland, Brisbane, Queensland, QLD 4072, Australia
Interests: data mining; big data analytics

Special Issue Information

Dear Colleagues,

The 4th International Conference on Big Data Analytics, Data Mining, and Computational Intelligence 2019 (BigDaCI’19) will be held in Porto, Portugal during 16–18 July 2019. The BigDaCI’19 conference is expected to provide an opportunity for researchers to meet and discuss the latest solutions, scientific results, and methods regarding solving intriguing problems in the fields of big data analytics, intelligent agents, and computational intelligence and their applications in science, technology, business, and commerce. For more detail, please check http://www.bigdaci.org/call-for-papers/

The scope and topics of interest of the Special Issue papers follow those BigDaCI’19 and are listed below:

  • Big data algorithms and architectures;
  • Computational intelligent frameworks for big data processing;
  • Data mining topics and applications.

The authors of a number of selected full papers of high quality will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Information published by MDPI in open access. The selection of these best papers will be based on their ratings in the conference review process, the quality of their presentation during the conference, and the impact they will be expected to have on the research community.

The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Submitted papers should be extended to the size of regular research or review articles, with 50% extension of new results. All submitted papers will undergo our standard peer-review procedure.

Accepted papers will be published in open-access format in Information and collected together in this Special Issue website. We would like to publish the extended best papers of the conference with Article Processing Fees waived. The deadline for submission to this Special Issue is 20 November 2019.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Prof. Dr. Ajith Abraham
Prof. Dr. Pedro Isaias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

11 pages, 852 KiB  
Article
Updating the Reduct in Fuzzy β-Covering via Matrix Approaches While Adding and Deleting Some Objects of the Universe
by Jianxin Huang, Peiqiu Yu and Weikang Li
Information 2020, 11(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/info11010003 - 19 Dec 2019
Cited by 11 | Viewed by 2018
Abstract
Since fuzzy β -covering was proposed, few papers have focused on how to calculate the reduct in fuzzy β -covering and how to update the reduct while adding and deleting some objects of the universe. Here, we propose a matrix-based approach for computing [...] Read more.
Since fuzzy β -covering was proposed, few papers have focused on how to calculate the reduct in fuzzy β -covering and how to update the reduct while adding and deleting some objects of the universe. Here, we propose a matrix-based approach for computing the reduct in a fuzzy β -covering and updating it dynamically using a matrix. First, matrix forms for computing the reduct in a fuzzy β -covering are proposed. Second, properties of the matrix-based approaches are studied while adding and deleting objects. Then, matrix-based algorithms for updating the reduct in a fuzzy β -covering are proposed. Finally, the efficiency and validity of the designed algorithms are verified by experiments. Full article
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
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23 pages, 325 KiB  
Article
Some Similarity Measures for Interval-Valued Picture Fuzzy Sets and Their Applications in Decision Making
by Peide Liu, Muhammad Munir, Tahir Mahmood and Kifayat Ullah
Information 2019, 10(12), 369; https://0-doi-org.brum.beds.ac.uk/10.3390/info10120369 - 25 Nov 2019
Cited by 36 | Viewed by 3545
Abstract
Similarity measures, distance measures and entropy measures are some common tools considered to be applied to some interesting real-life phenomena including pattern recognition, decision making, medical diagnosis and clustering. Further, interval-valued picture fuzzy sets (IVPFSs) are effective and useful to describe the fuzzy [...] Read more.
Similarity measures, distance measures and entropy measures are some common tools considered to be applied to some interesting real-life phenomena including pattern recognition, decision making, medical diagnosis and clustering. Further, interval-valued picture fuzzy sets (IVPFSs) are effective and useful to describe the fuzzy information. Therefore, this manuscript aims to develop some similarity measures for IVPFSs due to the significance of describing the membership grades of picture fuzzy set in terms of intervals. Several types cosine similarity measures, cotangent similarity measures, set-theoretic and grey similarity measures, four types of dice similarity measures and generalized dice similarity measures are developed. All the developed similarity measures are validated, and their properties are demonstrated. Two well-known problems, including mineral field recognition problems and multi-attribute decision making problems, are solved using the newly developed similarity measures. The superiorities of developed similarity measures over the similarity measures of picture fuzzy sets, interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets are demonstrated through a comparison and numerical examples. Full article
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
21 pages, 1458 KiB  
Article
Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews
by Muhammad Bilal, Mohsen Marjani, Ibrahim Abaker Targio Hashem, Abdullah Gani, Misbah Liaqat and Kwangman Ko
Information 2019, 10(10), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/info10100295 - 24 Sep 2019
Cited by 8 | Viewed by 3296
Abstract
With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success [...] Read more.
With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses. Full article
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
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15 pages, 623 KiB  
Article
Data Consistency Theory and Case Study for Scientific Big Data
by Peng Shi, Yulin Cui, Kangming Xu, Mingmei Zhang and Lianhong Ding
Information 2019, 10(4), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/info10040137 - 12 Apr 2019
Cited by 10 | Viewed by 7761
Abstract
Big data technique is a series of novel technologies to deal with large amounts of data from various sources. Unfortunately, it is inevitable that the data from different sources conflict with each other from the aspects of format, semantics, and value. To solve [...] Read more.
Big data technique is a series of novel technologies to deal with large amounts of data from various sources. Unfortunately, it is inevitable that the data from different sources conflict with each other from the aspects of format, semantics, and value. To solve the problem of conflicts, the paper proposes data consistency theory for scientific big data, including the basic concepts, properties, and quantitative evaluation method. Data consistency can be divided into different grades as complete consistency, strong consistency, weak consistency, and conditional consistency according to consistency degree and application demand. The case study is executed on material creep testing data. The analysis results show that the theory can solve the problem of conflicts in scientific big data. Full article
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
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